Papers › Global Entity Disambiguation with BERT

Global Entity Disambiguation with BERT

1 Sep 2019NAACL 2022 7arXiv:1909.00426archive 2025-07-28

Ikuya Yamada, Koki Washio, Hiroyuki Shindo, Yuji Matsumoto

We propose a global entity disambiguation (ED) model based on BERT. To capture global contextual information for ED, our model treats not only words but also entities as input tokens, and solves the task by sequentially resolving mentions to their referent entities and using resolved entities as inputs at each step. We train the model using a large entity-annotated corpus obtained from Wikipedia. We achieve new state-of-the-art results on five standard ED datasets: AIDA-CoNLL, MSNBC, AQUAINT, ACE2004, and WNED-WIKI. The source code and model checkpoint are available at https://github.com/studio-ousia/luke.

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Code

studio-ousia/luke officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Entity DisambiguationLanguage Modelling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Entity Disambiguation ACE2004 confidence-order Micro-F1 91.9 #2 of 6 Archive leaderboard report
Entity Disambiguation AIDA-CoNLL confidence-order In-KB Accuracy 95.0 #1 of 20 Archive leaderboard report
Entity Disambiguation AQUAINT confidence-order Micro-F1 93.5 #1 of 6 Archive leaderboard report
Entity Disambiguation MSNBC confidence-order Micro-F1 96.3 #1 of 6 Archive leaderboard report
Entity Disambiguation WNED-CWEB confidence-order Micro-F1 78.9 #2 of 7 Archive leaderboard report
Entity Disambiguation WNED-CWEB MEP Micro-F1 76.2 #6 of 7 Archive leaderboard report
Entity Disambiguation WNED-WIKI confidence-order Micro-F1 89.1 #2 of 7 Archive leaderboard report
Entity Disambiguation WNED-WIKI MEP Micro-F1 86.2 #5 of 7 Archive leaderboard report

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Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTransformer

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